Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 18 Dec 2014 23:37:48 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/18/t1418945887orcvuibgl0c4xwi.htm/, Retrieved Fri, 17 May 2024 17:09:10 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271325, Retrieved Fri, 17 May 2024 17:09:10 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [multiple regressi...] [2014-12-18 23:37:48] [8aa9b0b9e9cdf95f84c1d02ac9593640] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

7.5 21 54 255 0
2.5 26 36 132 0
6 22 16 92 1
6.5 22 40 171 0
1 18 27 117 1
1 23 61 219 1
5.5 12 69 279 1
8.5 20 34 148 0
6.5 22 21 130 1
4.5 21 34 181 1
2 19 34 234 1
5 22 13 85 1
0.5 15 12 66 1
5 20 51 236 1
5 19 19 135 0
2.5 18 81 218 0
5.5 20 42 199 1
3.5 21 22 112 0
4 15 85 278 0
0.5 16 25 113 1
6.5 23 22 84 1
4.5 21 19 86 0
7.5 18 45 222 1
5.5 25 45 167 1
4 9 51 207 1
4 23 24 85 1
5.5 16 73 237 0
2.5 16 24 102 0
5.5 19 61 221 0
0.5 25 23 128 1
3.5 25 14 91 1
2.5 18 54 198 1
4.5 23 36 138 1
4.5 21 26 196 1
4.5 10 30 139 0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271325&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271325&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271325&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

Multiple Linear Regression - Estimated Regression Equation
EX_res[t] = + 1.53157 + 0.0902477NUMERACYTOT[t] -0.051955comp.[t] + 0.0226496gebl_ber.[t] -1.23741geslacht_bin[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
EX_res[t] =  +  1.53157 +  0.0902477NUMERACYTOT[t] -0.051955comp.[t] +  0.0226496gebl_ber.[t] -1.23741geslacht_bin[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271325&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]EX_res[t] =  +  1.53157 +  0.0902477NUMERACYTOT[t] -0.051955comp.[t] +  0.0226496gebl_ber.[t] -1.23741geslacht_bin[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271325&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271325&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
EX_res[t] = + 1.53157 + 0.0902477NUMERACYTOT[t] -0.051955comp.[t] + 0.0226496gebl_ber.[t] -1.23741geslacht_bin[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	1.53157	2.34144	0.6541	0.51802	0.25901
NUMERACYTOT	0.0902477	0.0919192	0.9818	0.334042	0.167021
comp.	-0.051955	0.0385184	-1.349	0.187484	0.0937419
gebl_ber.	0.0226496	0.0119968	1.888	0.0687375	0.0343688
geslacht_bin	-1.23741	0.765694	-1.616	0.116548	0.0582742

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1.53157 & 2.34144 & 0.6541 & 0.51802 & 0.25901 \tabularnewline
NUMERACYTOT & 0.0902477 & 0.0919192 & 0.9818 & 0.334042 & 0.167021 \tabularnewline
comp. & -0.051955 & 0.0385184 & -1.349 & 0.187484 & 0.0937419 \tabularnewline
gebl_ber. & 0.0226496 & 0.0119968 & 1.888 & 0.0687375 & 0.0343688 \tabularnewline
geslacht_bin & -1.23741 & 0.765694 & -1.616 & 0.116548 & 0.0582742 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271325&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1.53157[/C][C]2.34144[/C][C]0.6541[/C][C]0.51802[/C][C]0.25901[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0902477[/C][C]0.0919192[/C][C]0.9818[/C][C]0.334042[/C][C]0.167021[/C][/ROW]
[ROW][C]comp.[/C][C]-0.051955[/C][C]0.0385184[/C][C]-1.349[/C][C]0.187484[/C][C]0.0937419[/C][/ROW]
[ROW][C]gebl_ber.[/C][C]0.0226496[/C][C]0.0119968[/C][C]1.888[/C][C]0.0687375[/C][C]0.0343688[/C][/ROW]
[ROW][C]geslacht_bin[/C][C]-1.23741[/C][C]0.765694[/C][C]-1.616[/C][C]0.116548[/C][C]0.0582742[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271325&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271325&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	1.53157	2.34144	0.6541	0.51802	0.25901
NUMERACYTOT	0.0902477	0.0919192	0.9818	0.334042	0.167021
comp.	-0.051955	0.0385184	-1.349	0.187484	0.0937419
gebl_ber.	0.0226496	0.0119968	1.888	0.0687375	0.0343688
geslacht_bin	-1.23741	0.765694	-1.616	0.116548	0.0582742

Multiple Linear Regression - Regression Statistics
Multiple R	0.405337
R-squared	0.164298
Adjusted R-squared	0.0528715
F-TEST (value)	1.4745
F-TEST (DF numerator)	4
F-TEST (DF denominator)	30
p-value	0.234642
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.03858
Sum Squared Residuals	124.675

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.405337 \tabularnewline
R-squared & 0.164298 \tabularnewline
Adjusted R-squared & 0.0528715 \tabularnewline
F-TEST (value) & 1.4745 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 30 \tabularnewline
p-value & 0.234642 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.03858 \tabularnewline
Sum Squared Residuals & 124.675 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271325&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.405337[/C][/ROW]
[ROW][C]R-squared[/C][C]0.164298[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0528715[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.4745[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]30[/C][/ROW]
[ROW][C]p-value[/C][C]0.234642[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.03858[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]124.675[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271325&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271325&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R	0.405337
R-squared	0.164298
Adjusted R-squared	0.0528715
F-TEST (value)	1.4745
F-TEST (DF numerator)	4
F-TEST (DF denominator)	30
p-value	0.234642
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.03858
Sum Squared Residuals	124.675

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	7.5	6.39684	1.10316
2	2.5	4.99737	-2.49737
3	6	3.53209	2.46791
4	6.5	5.3119	1.1881
5	1	3.16583	-2.16583
6	1	4.16085	-3.16085
7	5.5	4.11146	1.38854
8	8.5	4.92219	3.57781
9	6.5	4.133	2.367
10	4.5	4.52246	-0.0224593
11	2	5.54239	-3.54239
12	5	3.52941	1.47059
13	0.5	2.51928	-2.01928
14	5	4.7947	0.205298
15	5	5.31682	-0.316824
16	2.5	3.88528	-1.38528
17	5.5	4.42426	1.07574
18	3.5	4.82052	-1.32052
19	4	4.76569	-0.765688
20	0.5	2.99865	-2.49865
21	6.5	3.12941	3.37059
22	4.5	4.38749	0.112508
23	7.5	4.60884	2.89116
24	5.5	3.99485	1.50515
25	4	3.14514	0.854861
26	4	3.04815	0.951852
27	5.5	4.55076	0.949235
28	2.5	4.03887	-1.53887
29	5.5	5.08258	0.417425
30	0.5	4.25453	-3.75453
31	3.5	3.88409	-0.384091
32	2.5	3.59766	-1.09766
33	4.5	3.62511	0.874886
34	4.5	5.27784	-0.777843
35	4.5	4.02369	0.476312

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.5 & 6.39684 & 1.10316 \tabularnewline
2 & 2.5 & 4.99737 & -2.49737 \tabularnewline
3 & 6 & 3.53209 & 2.46791 \tabularnewline
4 & 6.5 & 5.3119 & 1.1881 \tabularnewline
5 & 1 & 3.16583 & -2.16583 \tabularnewline
6 & 1 & 4.16085 & -3.16085 \tabularnewline
7 & 5.5 & 4.11146 & 1.38854 \tabularnewline
8 & 8.5 & 4.92219 & 3.57781 \tabularnewline
9 & 6.5 & 4.133 & 2.367 \tabularnewline
10 & 4.5 & 4.52246 & -0.0224593 \tabularnewline
11 & 2 & 5.54239 & -3.54239 \tabularnewline
12 & 5 & 3.52941 & 1.47059 \tabularnewline
13 & 0.5 & 2.51928 & -2.01928 \tabularnewline
14 & 5 & 4.7947 & 0.205298 \tabularnewline
15 & 5 & 5.31682 & -0.316824 \tabularnewline
16 & 2.5 & 3.88528 & -1.38528 \tabularnewline
17 & 5.5 & 4.42426 & 1.07574 \tabularnewline
18 & 3.5 & 4.82052 & -1.32052 \tabularnewline
19 & 4 & 4.76569 & -0.765688 \tabularnewline
20 & 0.5 & 2.99865 & -2.49865 \tabularnewline
21 & 6.5 & 3.12941 & 3.37059 \tabularnewline
22 & 4.5 & 4.38749 & 0.112508 \tabularnewline
23 & 7.5 & 4.60884 & 2.89116 \tabularnewline
24 & 5.5 & 3.99485 & 1.50515 \tabularnewline
25 & 4 & 3.14514 & 0.854861 \tabularnewline
26 & 4 & 3.04815 & 0.951852 \tabularnewline
27 & 5.5 & 4.55076 & 0.949235 \tabularnewline
28 & 2.5 & 4.03887 & -1.53887 \tabularnewline
29 & 5.5 & 5.08258 & 0.417425 \tabularnewline
30 & 0.5 & 4.25453 & -3.75453 \tabularnewline
31 & 3.5 & 3.88409 & -0.384091 \tabularnewline
32 & 2.5 & 3.59766 & -1.09766 \tabularnewline
33 & 4.5 & 3.62511 & 0.874886 \tabularnewline
34 & 4.5 & 5.27784 & -0.777843 \tabularnewline
35 & 4.5 & 4.02369 & 0.476312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271325&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]7.5[/C][C]6.39684[/C][C]1.10316[/C][/ROW]
[ROW][C]2[/C][C]2.5[/C][C]4.99737[/C][C]-2.49737[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]3.53209[/C][C]2.46791[/C][/ROW]
[ROW][C]4[/C][C]6.5[/C][C]5.3119[/C][C]1.1881[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]3.16583[/C][C]-2.16583[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]4.16085[/C][C]-3.16085[/C][/ROW]
[ROW][C]7[/C][C]5.5[/C][C]4.11146[/C][C]1.38854[/C][/ROW]
[ROW][C]8[/C][C]8.5[/C][C]4.92219[/C][C]3.57781[/C][/ROW]
[ROW][C]9[/C][C]6.5[/C][C]4.133[/C][C]2.367[/C][/ROW]
[ROW][C]10[/C][C]4.5[/C][C]4.52246[/C][C]-0.0224593[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]5.54239[/C][C]-3.54239[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]3.52941[/C][C]1.47059[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]2.51928[/C][C]-2.01928[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]4.7947[/C][C]0.205298[/C][/ROW]
[ROW][C]15[/C][C]5[/C][C]5.31682[/C][C]-0.316824[/C][/ROW]
[ROW][C]16[/C][C]2.5[/C][C]3.88528[/C][C]-1.38528[/C][/ROW]
[ROW][C]17[/C][C]5.5[/C][C]4.42426[/C][C]1.07574[/C][/ROW]
[ROW][C]18[/C][C]3.5[/C][C]4.82052[/C][C]-1.32052[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.76569[/C][C]-0.765688[/C][/ROW]
[ROW][C]20[/C][C]0.5[/C][C]2.99865[/C][C]-2.49865[/C][/ROW]
[ROW][C]21[/C][C]6.5[/C][C]3.12941[/C][C]3.37059[/C][/ROW]
[ROW][C]22[/C][C]4.5[/C][C]4.38749[/C][C]0.112508[/C][/ROW]
[ROW][C]23[/C][C]7.5[/C][C]4.60884[/C][C]2.89116[/C][/ROW]
[ROW][C]24[/C][C]5.5[/C][C]3.99485[/C][C]1.50515[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]3.14514[/C][C]0.854861[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]3.04815[/C][C]0.951852[/C][/ROW]
[ROW][C]27[/C][C]5.5[/C][C]4.55076[/C][C]0.949235[/C][/ROW]
[ROW][C]28[/C][C]2.5[/C][C]4.03887[/C][C]-1.53887[/C][/ROW]
[ROW][C]29[/C][C]5.5[/C][C]5.08258[/C][C]0.417425[/C][/ROW]
[ROW][C]30[/C][C]0.5[/C][C]4.25453[/C][C]-3.75453[/C][/ROW]
[ROW][C]31[/C][C]3.5[/C][C]3.88409[/C][C]-0.384091[/C][/ROW]
[ROW][C]32[/C][C]2.5[/C][C]3.59766[/C][C]-1.09766[/C][/ROW]
[ROW][C]33[/C][C]4.5[/C][C]3.62511[/C][C]0.874886[/C][/ROW]
[ROW][C]34[/C][C]4.5[/C][C]5.27784[/C][C]-0.777843[/C][/ROW]
[ROW][C]35[/C][C]4.5[/C][C]4.02369[/C][C]0.476312[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271325&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271325&T=4

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	7.5	6.39684	1.10316
2	2.5	4.99737	-2.49737
3	6	3.53209	2.46791
4	6.5	5.3119	1.1881
5	1	3.16583	-2.16583
6	1	4.16085	-3.16085
7	5.5	4.11146	1.38854
8	8.5	4.92219	3.57781
9	6.5	4.133	2.367
10	4.5	4.52246	-0.0224593
11	2	5.54239	-3.54239
12	5	3.52941	1.47059
13	0.5	2.51928	-2.01928
14	5	4.7947	0.205298
15	5	5.31682	-0.316824
16	2.5	3.88528	-1.38528
17	5.5	4.42426	1.07574
18	3.5	4.82052	-1.32052
19	4	4.76569	-0.765688
20	0.5	2.99865	-2.49865
21	6.5	3.12941	3.37059
22	4.5	4.38749	0.112508
23	7.5	4.60884	2.89116
24	5.5	3.99485	1.50515
25	4	3.14514	0.854861
26	4	3.04815	0.951852
27	5.5	4.55076	0.949235
28	2.5	4.03887	-1.53887
29	5.5	5.08258	0.417425
30	0.5	4.25453	-3.75453
31	3.5	3.88409	-0.384091
32	2.5	3.59766	-1.09766
33	4.5	3.62511	0.874886
34	4.5	5.27784	-0.777843
35	4.5	4.02369	0.476312

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.80096	0.398081	0.19904
9	0.70928	0.58144	0.29072
10	0.631769	0.736462	0.368231
11	0.921611	0.156779	0.0783894
12	0.884019	0.231962	0.115981
13	0.94075	0.1185	0.05925
14	0.902542	0.194916	0.097458
15	0.86254	0.27492	0.13746
16	0.825828	0.348344	0.174172
17	0.767296	0.465408	0.232704
18	0.703362	0.593276	0.296638
19	0.639332	0.721337	0.360668
20	0.689397	0.621206	0.310603
21	0.823833	0.352334	0.176167
22	0.754172	0.491655	0.245828
23	0.845718	0.308564	0.154282
24	0.810003	0.379994	0.189997
25	0.699711	0.600577	0.300289
26	0.627909	0.744182	0.372091
27	0.457641	0.915281	0.542359

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.80096 & 0.398081 & 0.19904 \tabularnewline
9 & 0.70928 & 0.58144 & 0.29072 \tabularnewline
10 & 0.631769 & 0.736462 & 0.368231 \tabularnewline
11 & 0.921611 & 0.156779 & 0.0783894 \tabularnewline
12 & 0.884019 & 0.231962 & 0.115981 \tabularnewline
13 & 0.94075 & 0.1185 & 0.05925 \tabularnewline
14 & 0.902542 & 0.194916 & 0.097458 \tabularnewline
15 & 0.86254 & 0.27492 & 0.13746 \tabularnewline
16 & 0.825828 & 0.348344 & 0.174172 \tabularnewline
17 & 0.767296 & 0.465408 & 0.232704 \tabularnewline
18 & 0.703362 & 0.593276 & 0.296638 \tabularnewline
19 & 0.639332 & 0.721337 & 0.360668 \tabularnewline
20 & 0.689397 & 0.621206 & 0.310603 \tabularnewline
21 & 0.823833 & 0.352334 & 0.176167 \tabularnewline
22 & 0.754172 & 0.491655 & 0.245828 \tabularnewline
23 & 0.845718 & 0.308564 & 0.154282 \tabularnewline
24 & 0.810003 & 0.379994 & 0.189997 \tabularnewline
25 & 0.699711 & 0.600577 & 0.300289 \tabularnewline
26 & 0.627909 & 0.744182 & 0.372091 \tabularnewline
27 & 0.457641 & 0.915281 & 0.542359 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271325&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.80096[/C][C]0.398081[/C][C]0.19904[/C][/ROW]
[ROW][C]9[/C][C]0.70928[/C][C]0.58144[/C][C]0.29072[/C][/ROW]
[ROW][C]10[/C][C]0.631769[/C][C]0.736462[/C][C]0.368231[/C][/ROW]
[ROW][C]11[/C][C]0.921611[/C][C]0.156779[/C][C]0.0783894[/C][/ROW]
[ROW][C]12[/C][C]0.884019[/C][C]0.231962[/C][C]0.115981[/C][/ROW]
[ROW][C]13[/C][C]0.94075[/C][C]0.1185[/C][C]0.05925[/C][/ROW]
[ROW][C]14[/C][C]0.902542[/C][C]0.194916[/C][C]0.097458[/C][/ROW]
[ROW][C]15[/C][C]0.86254[/C][C]0.27492[/C][C]0.13746[/C][/ROW]
[ROW][C]16[/C][C]0.825828[/C][C]0.348344[/C][C]0.174172[/C][/ROW]
[ROW][C]17[/C][C]0.767296[/C][C]0.465408[/C][C]0.232704[/C][/ROW]
[ROW][C]18[/C][C]0.703362[/C][C]0.593276[/C][C]0.296638[/C][/ROW]
[ROW][C]19[/C][C]0.639332[/C][C]0.721337[/C][C]0.360668[/C][/ROW]
[ROW][C]20[/C][C]0.689397[/C][C]0.621206[/C][C]0.310603[/C][/ROW]
[ROW][C]21[/C][C]0.823833[/C][C]0.352334[/C][C]0.176167[/C][/ROW]
[ROW][C]22[/C][C]0.754172[/C][C]0.491655[/C][C]0.245828[/C][/ROW]
[ROW][C]23[/C][C]0.845718[/C][C]0.308564[/C][C]0.154282[/C][/ROW]
[ROW][C]24[/C][C]0.810003[/C][C]0.379994[/C][C]0.189997[/C][/ROW]
[ROW][C]25[/C][C]0.699711[/C][C]0.600577[/C][C]0.300289[/C][/ROW]
[ROW][C]26[/C][C]0.627909[/C][C]0.744182[/C][C]0.372091[/C][/ROW]
[ROW][C]27[/C][C]0.457641[/C][C]0.915281[/C][C]0.542359[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271325&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271325&T=5

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.80096	0.398081	0.19904
9	0.70928	0.58144	0.29072
10	0.631769	0.736462	0.368231
11	0.921611	0.156779	0.0783894
12	0.884019	0.231962	0.115981
13	0.94075	0.1185	0.05925
14	0.902542	0.194916	0.097458
15	0.86254	0.27492	0.13746
16	0.825828	0.348344	0.174172
17	0.767296	0.465408	0.232704
18	0.703362	0.593276	0.296638
19	0.639332	0.721337	0.360668
20	0.689397	0.621206	0.310603
21	0.823833	0.352334	0.176167
22	0.754172	0.491655	0.245828
23	0.845718	0.308564	0.154282
24	0.810003	0.379994	0.189997
25	0.699711	0.600577	0.300289
26	0.627909	0.744182	0.372091
27	0.457641	0.915281	0.542359

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271325&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271325&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271325&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code